Automated Data Analysis Report
This report provides a detailed visual and statistical analysis of the given dataset, using various techniques like correlation heatmaps, histograms, scatter plots, and linear regressions.
1. Correlation Heatmap
This heatmap shows the correlation between numerical columns in the dataset. A value close to 1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation. A value close to 0 means no correlation.
2. Descriptive Statistics
The table below summarizes the basic statistics for each numerical column, including the count, mean, standard deviation, and the minimum and maximum values.
| Column | Count | Mean | Std | Min | 25% | 50% | 75% | Max |
|---|---|---|---|---|---|---|---|---|
| A_id | 4000.0 | 1999.5 | 1154.8448669265786 | 0.0 | 999.75 | 1999.5 | 2999.25 | 3999.0 |
| Size | 4000.0 | -0.50301462982675 | 1.928058688854979 | -7.151703059 | -1.816764527 | -0.5137025125000001 | 0.8055264495000001 | 6.406366899 |
| Weight | 4000.0 | -0.9895465445945 | 1.6025072141517547 | -7.149847675 | -2.01177029275 | -0.9847364865 | 0.03097644 | 5.79071359 |
| Sweetness | 4000.0 | -0.47047851978824995 | 1.943440658920452 | -6.894485494 | -1.7384250625 | -0.5047584635 | 0.8019219209999999 | 6.374915513 |
| Crunchiness | 4000.0 | 0.9854779038585 | 1.402757204211963 | -6.055057805 | 0.06276439525000001 | 0.9982494390000001 | 1.8942342170000002 | 7.619851801 |
| Juiciness | 4000.0 | 0.5121179684932501 | 1.9302856730942946 | -5.961897048 | -0.80128581525 | 0.5342186584999999 | 1.8359763875 | 7.364402864 |
| Ripeness | 4000.0 | 0.4982774280305 | 1.8744267757033417 | -5.864598918 | -0.7716768665 | 0.5034447135 | 1.76621164075 | 7.237836684 |
3. Histograms
Histograms provide an overview of the distribution of values for each numerical column. The density curve helps visualize the probability distribution of the data.
A_id
Size
Weight
Sweetness
Crunchiness
Juiciness
Ripeness
4. Scatter Plots
Scatter plots show the relationship between two numerical variables. A pattern can help identify correlations, trends, or outliers.
A_id_vs_Size
A_id_vs_Weight
A_id_vs_Sweetness
A_id_vs_Crunchiness
A_id_vs_Juiciness
A_id_vs_Ripeness
Size_vs_Weight
Size_vs_Sweetness
Size_vs_Crunchiness
Size_vs_Juiciness
Size_vs_Ripeness
Weight_vs_Sweetness
Weight_vs_Crunchiness
Weight_vs_Juiciness
Weight_vs_Ripeness
Sweetness_vs_Crunchiness
Sweetness_vs_Juiciness
Sweetness_vs_Ripeness
Crunchiness_vs_Juiciness
Crunchiness_vs_Ripeness
Juiciness_vs_Ripeness
5. Regression Analysis
Linear regression attempts to model the relationship between two numerical variables by fitting a line through the data points. The red line in the graphs represents the linear fit.
A_id_reg_Size
A_id_reg_Weight
A_id_reg_Sweetness
A_id_reg_Crunchiness
A_id_reg_Juiciness
A_id_reg_Ripeness
Size_reg_A_id
Size_reg_Weight
Size_reg_Sweetness
Size_reg_Crunchiness
Size_reg_Juiciness
Size_reg_Ripeness
Weight_reg_A_id
Weight_reg_Size
Weight_reg_Sweetness
Weight_reg_Crunchiness
Weight_reg_Juiciness
Weight_reg_Ripeness
Sweetness_reg_A_id
Sweetness_reg_Size
Sweetness_reg_Weight
Sweetness_reg_Crunchiness
Sweetness_reg_Juiciness
Sweetness_reg_Ripeness
Crunchiness_reg_A_id
Crunchiness_reg_Size
Crunchiness_reg_Weight
Crunchiness_reg_Sweetness
Crunchiness_reg_Juiciness
Crunchiness_reg_Ripeness
Juiciness_reg_A_id
Juiciness_reg_Size
Juiciness_reg_Weight
Juiciness_reg_Sweetness
Juiciness_reg_Crunchiness
Juiciness_reg_Ripeness
Ripeness_reg_A_id
Ripeness_reg_Size
Ripeness_reg_Weight
Ripeness_reg_Sweetness
Ripeness_reg_Crunchiness
Ripeness_reg_Juiciness
